| Variational bayesian blind deconvolution using a total variation prior. | |
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MedLine Citation:
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PMID: 19095515 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters. |
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Authors:
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S Derin Babacan; Rafael Molina; Aggelos K Katsaggelos |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Volume: 18 ISSN: 1057-7149 ISO Abbreviation: IEEE Trans Image Process Publication Date: 2009 Jan |
Date Detail:
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Created Date: 2008-12-19 Completed Date: 2009-02-19 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9886191 Medline TA: IEEE Trans Image Process Country: United States |
Other Details:
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Languages: eng Pagination: 12-26 Citation Subset: IM |
Affiliation:
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Department of Electrical Engineering and Computer Science, Northwestern University, IL 60208-3118, USA. sdb@northwestern.edu |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artifacts* Artificial Intelligence* Bayes Theorem Computer Simulation Image Enhancement / methods* Image Interpretation, Computer-Assisted / methods* Models, Statistical Pattern Recognition, Automated / methods* Reproducibility of Results Sensitivity and Specificity |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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